# Moto: Enhancing Embedding with Multiple Joint Factors for Chinese Text Classification

arXiv: https://arxiv.org/abs/2212.08105 · HTML: https://mlsanity.com/paper/2212.08105/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 9 Dec 2022.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Component-Enhanced Chinese Character Embeddings](https://mlsanity.com/paper/1508.06669/) (2015)
- [Neural Word Segmentation Learning for Chinese](https://mlsanity.com/paper/1606.04300/) (2016)
- [Fast and Accurate Neural Word Segmentation for Chinese](https://mlsanity.com/paper/1704.07047/) (2017)
- [Multiple Character Embeddings for Chinese Word Segmentation](https://mlsanity.com/paper/1808.04963/) (2018)

## Built on by (most cited first, 1 of 1)

- [Emotion Detection in Unfix-length-Context Conversation](https://mlsanity.com/paper/2302.06029/) (2023)

## Abstract (arXiv)

Recently, language representation techniques have achieved great performances in text classification. However, most existing representation models are specifically designed for English materials, which may fail in Chinese because of the huge difference between these two languages. Actually, few existing methods for Chinese text classification process texts at a single level. However, as a special kind of hieroglyphics, radicals of Chinese characters are good semantic carriers. In addition, Pinyin codes carry the semantic of tones, and Wubi reflects the stroke structure information, textitetc. Unfortunately, previous researches neglected to find an effective way to distill the useful parts of these four factors and to fuse them. In our works, we propose a novel model called Moto: Enhancing Embedding with Multiple Joint Factors. Specifically, we design an attention mechanism to distill the useful parts by fusing the four-level information above more effectively. We conduct extensive experiments on four popular tasks. The empirical results show that our Moto achieves SOTA 0.8316 (F1-score, 2.11% improvement) on Chinese news titles, 96.38 (1.24% improvement) on Fudan Corpus and 0.9633 (3.26% improvement) on THUCNews.